Towards a nationwide growth and yield model for radiata pine plantations in New Zealand
Bibliographic record
Abstract
A study was designed to evaluate a forest stand modelling approach for management use that can be applied across a wide range of site types and climatic regions. Radiata pine (Pinus radiata D. Don) plantations dominate New Zealand's forestry sector; thus, this species was the subject of the study. A major goal of the study was to compare different modelling approaches, which combine simplicity and site sensitivity. Therefore, two general modelling approaches were investigated: a site-stratified and a physiological hybrid approach. Both approaches were implemented by using difference equations. The investigation revealed more consistently improved fits of stratified models, although the fitting process showed potential bias of parameter estimates. On the other hand, the hybrid approach resulted in promising results, especially for stand basal area. The introduction of climate and site variables showed less improvement for mean top height than for basal area. The application of the model on regional scales resulted in an improved prediction in a region with plenty of growth limitations, but less precise results in a region where growth was limited primarily by light and temperature. In the whole, results of the hybrid approach will encourage further studies that incorporate more sophisticated approaches for depicting physiological processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".